{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing StackOverflow tags\n",
    "\n",
    "[StackOverflow](http://stackoverflow.com) is a question-and-answer website for programmers. Each question can have up to 5 descriptive tags for people to be able to find related tags. In this demo, we will look at 50,000 questions with more than 3 tags to try to visualize what StackOverflow is all about. The data came from http://data.stackexchange.com/stackoverflow/query/new with a query like this: \n",
    "\n",
    "```\n",
    "select tags from posts where PostTypeId=1 and tags like '%><%><%'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook cells below use `pymldb`'s `Connection` class to make [REST API](../../../../doc/#builtin/WorkingWithRest.md.html) calls. You can check out the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymldb import Connection\n",
    "mldb = Connection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sequence of procedures below is based on the one explained in the [Mapping Reddit](../../../../doc/nblink.html#_demos/Mapping Reddit) demo notebook. In pseudo-code:\n",
    "\n",
    "```\n",
    "dataset so_raw = import(\"http://public.mldb.ai/stackoverflow.csv.gz\")\n",
    "dataset so_tags = tokenize(so_raw)\n",
    "dataset so_counts = count(transpose(so_tags))\n",
    "dataset so_svd_embedding = svd_embed(so_tags)\n",
    "dataset so_kmeans_clusters = kmeans_cluster(so_svd_embedding)\n",
    "dataset so_tsne_embedding = tsne_embed(so_svd_embedding)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [201]>\n",
      "<Response [201]>\n",
      "<Response [201]>\n",
      "<Response [201]>\n",
      "<Response [201]>\n",
      "<Response [201]>\n"
     ]
    }
   ],
   "source": [
    "# dataset so_raw = import(\"http://public.mldb.ai/stackoverflow.csv.gz\")\n",
    "print mldb.put('/v1/procedures/import_so_raw', {\n",
    "    \"type\": \"import.text\",\n",
    "    \"params\": {\n",
    "        \"dataFileUrl\": \"http://public.mldb.ai/stackoverflow.csv.gz\",\n",
    "        'delimiter':'', \n",
    "        'quoteChar':'',\n",
    "        'outputDataset': 'so_raw',\n",
    "        'runOnCreation': True\n",
    "    }\n",
    "})\n",
    "\n",
    "# dataset so_tags = tokenize(so_raw)\n",
    "print mldb.post('/v1/procedures', {\n",
    "    \"id\": \"so_import\",\n",
    "    \"type\": \"transform\",\n",
    "    \"params\": {\n",
    "        \"inputData\": \"select tokenize(lineText, {splitChars: '\\\"\\r<>', quoteChar: ''}) as * from so_raw\",\n",
    "        \"outputDataset\": \"so_tags\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n",
    "\n",
    "# dataset so_counts = count(transpose(so_tags))\n",
    "print mldb.post('/v1/procedures', {\n",
    "    \"id\": \"so_counts\",\n",
    "    \"type\": \"transform\",\n",
    "    \"params\": {\n",
    "        \"inputData\": \"select columnCount() AS numQuestions from transpose(so_tags)\",\n",
    "        \"outputDataset\": \"so_counts\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n",
    "\n",
    "# dataset so_svd_embedding = svd_embed(so_tags)\n",
    "print mldb.post('/v1/procedures', {\n",
    "    \"id\": \"so_svd\",\n",
    "    \"type\" : \"svd.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"\"\"\n",
    "            select \n",
    "                COLUMN EXPR (AS columnName() ORDER BY rowCount() DESC, columnName() LIMIT 6000) \n",
    "            from so_tags\n",
    "        \"\"\",\n",
    "        \"columnOutputDataset\" : \"so_svd_embedding\",\n",
    "        \"numSingularValues\": 100,\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n",
    "\n",
    "# dataset so_kmeans_clusters = kmeans_cluster(so_svd_embedding)\n",
    "print mldb.post('/v1/procedures', {\n",
    "    \"id\" : \"so_kmeans\",\n",
    "    \"type\" : \"kmeans.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"select * from so_svd_embedding\",\n",
    "        \"outputDataset\" : \"so_kmeans_clusters\",\n",
    "        \"numClusters\" : 20,\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n",
    "\n",
    "# dataset so_tsne_embedding = tsne_embed(so_svd_embedding)\n",
    "print mldb.post('/v1/procedures', {\n",
    "    \"id\": \"so_tsne\",\n",
    "    \"type\" : \"tsne.train\",\n",
    "    \"params\" : {\n",
    "        \"trainingData\" : \"select * from so_svd_embedding\",\n",
    "        \"rowOutputDataset\" : \"so_tsne_embedding\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization with Bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the program above we built three datasets: \n",
    "\n",
    "* `so_tsne_embedding`: contains `x` and `y` coordinates per tag\n",
    "* `so_kmeans_clusters`: contains a `cluster` per tag\n",
    "* `so_counts`: contains `numQuestions` per tag\n",
    "\n",
    "Now we query out a `pandas` dataframe with the `merge()` of these three datasets, along with a 'grid' coordinate for each tag which we will use to automatically position labels in the visualization below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>grid_x</th>\n",
       "      <th>grid_y</th>\n",
       "      <th>numQuestions</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c#</th>\n",
       "      <td>8</td>\n",
       "      <td>-21</td>\n",
       "      <td>-14</td>\n",
       "      <td>5929</td>\n",
       "      <td>-18.530771</td>\n",
       "      <td>-13.916371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>java</th>\n",
       "      <td>8</td>\n",
       "      <td>-56</td>\n",
       "      <td>0</td>\n",
       "      <td>5449</td>\n",
       "      <td>-54.343040</td>\n",
       "      <td>2.447962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>javascript</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>-42</td>\n",
       "      <td>4250</td>\n",
       "      <td>23.090406</td>\n",
       "      <td>-41.900581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c++</th>\n",
       "      <td>8</td>\n",
       "      <td>-21</td>\n",
       "      <td>-14</td>\n",
       "      <td>3231</td>\n",
       "      <td>-22.746912</td>\n",
       "      <td>-13.542056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>android</th>\n",
       "      <td>2</td>\n",
       "      <td>-70</td>\n",
       "      <td>-28</td>\n",
       "      <td>3013</td>\n",
       "      <td>-69.587387</td>\n",
       "      <td>-29.107584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cluster  grid_x  grid_y  numQuestions          x          y\n",
       "_rowName                                                               \n",
       "c#                8     -21     -14          5929 -18.530771 -13.916371\n",
       "java              8     -56       0          5449 -54.343040   2.447962\n",
       "javascript        8      21     -42          4250  23.090406 -41.900581\n",
       "c++               8     -21     -14          3231 -22.746912 -13.542056\n",
       "android           2     -70     -28          3013 -69.587387 -29.107584"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mldb.query(\"\"\"\n",
    "    select c.* as *, m.* as *, quantize(m.x, 7) as grid_x, quantize(m.y, 7) as grid_y \n",
    "    named c.rowName()   \n",
    "    from merge(so_tsne_embedding, so_kmeans_clusters) as m\n",
    "        join so_counts as c on (c.rowName() = m.rowPathElement(0))\n",
    "    where m.cluster is not null\n",
    "    order by c.numQuestions desc\n",
    "    \"\"\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "colormap = np.array([\n",
    "    \"#1f77b4\", \"#aec7e8\", \"#ff7f0e\", \"#ffbb78\", \"#2ca02c\", \n",
    "    \"#98df8a\", \"#d62728\", \"#ff9896\", \"#9467bd\", \"#c5b0d5\", \n",
    "    \"#8c564b\", \"#c49c94\", \"#e377c2\", \"#f7b6d2\", \"#7f7f7f\", \n",
    "    \"#c7c7c7\", \"#bcbd22\", \"#dbdb8d\", \"#17becf\", \"#9edae5\"\n",
    "])\n",
    "\n",
    "import bokeh.plotting as bp\n",
    "from bokeh.models import HoverTool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"3e3b6031-bb0e-484e-9d38-daa617bee179\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#3e3b6031-bb0e-484e-9d38-daa617bee179\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this line must be in its own cell \n",
    "bp.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"6aac75b8-5853-473f-b182-4a1444284f92\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"6aac75b8-5853-473f-b182-4a1444284f92\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '6aac75b8-5853-473f-b182-4a1444284f92' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"057dd2d8-d72a-4509-9d60-12683d1decb4\",\"elementid\":\"6aac75b8-5853-473f-b182-4a1444284f92\",\"modelid\":\"f8c7d631-59de-43ba-accc-7d4b6fd18a75\",\"notebook_comms_target\":\"68b8ec9b-83f8-455a-8c2a-612b12bc867f\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
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       "      function(Bokeh) {\n",
       "      }\n",
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       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
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       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
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       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
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       "    }\n",
       "  }(this));\n",
       "</script>"
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    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[16]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7f142ac473d0>"
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   "source": [
    "x = bp.figure(plot_width=900, plot_height=700, title=\"Map of StackOverflow Tags\",\n",
    "       tools=[HoverTool( tooltips=[ (\"tag\", \"@tags\") ] )], toolbar_location=None,\n",
    "       x_axis_type=None, y_axis_type=None, min_border=1)\n",
    "x.scatter(\n",
    "    x = df.x.values, \n",
    "    y = df.y.values, \n",
    "    color=colormap[df.cluster.astype(int).values],\n",
    "    alpha=0.6,\n",
    "    radius=(df.numQuestions.values ** .35)/5,\n",
    "    source=bp.ColumnDataSource({\"tags\": df.index.values})\n",
    ")\n",
    "\n",
    "labels = df.reset_index().groupby(['grid_x', 'grid_y'], as_index=False).first()\n",
    "labels = labels[labels[\"numQuestions\"] > 200]\n",
    "x.text(\n",
    "    x = labels.x.values, \n",
    "    y = labels.y.values,\n",
    "    text = labels._rowName.values,\n",
    "    text_align=\"center\", text_baseline=\"middle\",\n",
    "    text_font_size=\"8pt\", text_font_style=\"bold\",\n",
    "    text_color=\"#333333\"\n",
    ")\n",
    "bp.show(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Where to next?\n",
    "\n",
    "Check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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